{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d8013313",
   "metadata": {},
   "source": [
    "## GReaT Example with California Housing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb5ba9dc-4372-45ff-80bb-e63fa99e1aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute only once!\n",
    "import os\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "os.chdir(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ce19c02-2b33-4f76-bdb5-01889292f6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "058f3bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import logging\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f78e5ef1-1180-4e81-878c-f2e248226795",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import set_logging_level\n",
    "from be_great import GReaT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd831932",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78fcfe3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = set_logging_level(logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95782c58",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3fe4f469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  MedHouseVal  \n",
       "0    -122.23        4.526  \n",
       "1    -122.22        3.585  \n",
       "2    -122.24        3.521  \n",
       "3    -122.25        3.413  \n",
       "4    -122.25        3.422  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = datasets.fetch_california_housing(as_frame=True).frame\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ff376c",
   "metadata": {},
   "source": [
    "### Create GReaT Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10bf87c-457d-4f9d-a26a-12bdbb4cb9b1",
   "metadata": {},
   "source": [
    "Only one epoch here for demonstration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee720651",
   "metadata": {},
   "outputs": [],
   "source": [
    "great = GReaT(\"distilgpt2\",                         # Name of the large language model used (see HuggingFace for more options)\n",
    "              epochs=1,                             # Number of epochs to train (only one epoch for demonstration)\n",
    "              save_steps=2000,                      # Save model weights every x steps\n",
    "              logging_steps=50,                     # Log the loss and learning rate every x steps\n",
    "              experiment_dir=\"trainer_california\",  # Name of the directory where all intermediate steps are saved\n",
    "              #lr_scheduler_type=\"constant\",        # Specify the learning rate scheduler \n",
    "              #learning_rate=5e-5                   # Set the inital learning rate\n",
    "             )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b19304d",
   "metadata": {},
   "source": [
    "### Start Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d7173938",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[39;20m2022-10-13 10:12:58,445 - INFO - Convert data into HuggingFace dataset object... (great.py:99)\u001b[0m\n",
      "\u001b[39;20m2022-10-13 10:12:58,451 - INFO - Create GReaT Trainer... (great.py:104)\u001b[0m\n",
      "\u001b[39;20m2022-10-13 10:13:00,554 - INFO - Start training... (great.py:113)\u001b[0m\n",
      "/home/kathrin/miniconda3/envs/transformers/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "***** Running training *****\n",
      "  Num examples = 20640\n",
      "  Num Epochs = 1\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1290\n",
      "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
      "/home/kathrin/miniconda3/envs/transformers/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1290' max='1290' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1290/1290 01:49, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>2.680700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>2.031900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>1.939400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>1.914700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>1.875900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>1.867900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>1.851000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>1.836000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>1.803000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550</td>\n",
       "      <td>1.806700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>1.810800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>1.815500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>1.797400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>750</td>\n",
       "      <td>1.778400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>1.800900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>850</td>\n",
       "      <td>1.793400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>1.794900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>950</td>\n",
       "      <td>1.794400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>1.777800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1050</td>\n",
       "      <td>1.773700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>1.769200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1150</td>\n",
       "      <td>1.781900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>1.772400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1250</td>\n",
       "      <td>1.769500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainer = great.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0554c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_runtime': 111.2294,\n",
       " 'train_samples_per_second': 185.562,\n",
       " 'train_steps_per_second': 11.598,\n",
       " 'total_flos': 464022201434112.0,\n",
       " 'train_loss': 1.854305208191391,\n",
       " 'epoch': 1.0,\n",
       " 'step': 1290}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_hist = trainer.state.log_history.copy()\n",
    "loss_hist.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ccc84e57",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = [x[\"loss\"] for x in loss_hist]\n",
    "epochs = [x[\"epoch\"] for x in loss_hist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc37dd63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f32cb9e6340>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744176a5",
   "metadata": {},
   "source": [
    "### Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7305e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "great.save(\"california\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396c6731",
   "metadata": {},
   "source": [
    "### Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa91661d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# great = GReaT.load_from_dir(\"california\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1612a7b2-a060-407c-80d8-bab46a96686a",
   "metadata": {},
   "outputs": [],
   "source": [
    "great.load_finetuned_model(\"../great_private/models/california/california_distilgpt2_100.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9ca066",
   "metadata": {},
   "source": [
    "### Generate Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b62cfebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "95b3f31d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1035it [00:09, 107.38it/s]                                                                                              \n"
     ]
    }
   ],
   "source": [
    "samples = great.sample(n_samples, k=50, device=\"cuda:1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dff8c01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0990</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.242424</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>2.856757</td>\n",
       "      <td>33.68</td>\n",
       "      <td>-117.88</td>\n",
       "      <td>4.09088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.1875</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.879051</td>\n",
       "      <td>1.041030</td>\n",
       "      <td>1127.0</td>\n",
       "      <td>2.446928</td>\n",
       "      <td>38.63</td>\n",
       "      <td>-121.35</td>\n",
       "      <td>1.53564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.6000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.837838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>693.0</td>\n",
       "      <td>2.790062</td>\n",
       "      <td>36.64</td>\n",
       "      <td>-120.84</td>\n",
       "      <td>1.10397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.8934</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.137055</td>\n",
       "      <td>0.945205</td>\n",
       "      <td>984.0</td>\n",
       "      <td>3.160714</td>\n",
       "      <td>38.00</td>\n",
       "      <td>-121.45</td>\n",
       "      <td>1.64981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.4861</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.694915</td>\n",
       "      <td>1.015965</td>\n",
       "      <td>867.0</td>\n",
       "      <td>2.251497</td>\n",
       "      <td>37.31</td>\n",
       "      <td>-122.46</td>\n",
       "      <td>1.09459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  6.0990      34.0  6.242424   1.000000      1758.0  2.856757     33.68   \n",
       "1  4.1875      31.0  5.879051   1.041030      1127.0  2.446928     38.63   \n",
       "2  3.6000      16.0  6.837838   1.000000       693.0  2.790062     36.64   \n",
       "3  4.8934      23.0  5.137055   0.945205       984.0  3.160714     38.00   \n",
       "4  2.4861      52.0  4.694915   1.015965       867.0  2.251497     37.31   \n",
       "\n",
       "   Longitude  MedHouseVal  \n",
       "0    -117.88      4.09088  \n",
       "1    -121.35      1.53564  \n",
       "2    -120.84      1.10397  \n",
       "3    -121.45      1.64981  \n",
       "4    -122.46      1.09459  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db334053-1166-409c-acb4-7bdf2243a836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 9)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc0bfd6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples.to_csv(\"california_samples.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9e2cd15",
   "metadata": {},
   "source": [
    "## Plot Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c529095",
   "metadata": {},
   "source": [
    "Original Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5ef00f79-cd0f-4e27-b11b-61f46bc37949",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_samples = data.sample(n = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d086807b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f32ca77d070>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(true_samples[\"Longitude\"], true_samples[\"Latitude\"], c=true_samples[\"MedHouseVal\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d014af",
   "metadata": {},
   "source": [
    "Generated samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc661470",
   "metadata": {},
   "outputs": [],
   "source": [
    "#samples = pd.read_csv(\"california_samples.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "54b5652d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f32ca692fa0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(samples[\"Longitude\"], samples[\"Latitude\"], c=samples[\"MedHouseVal\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "967789ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e86e1320",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03ca2c0e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "transformers",
   "language": "python",
   "name": "transformers"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
